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Distributed TensorFlow - O'Reilly Media
On the one hand: distributed tensorflow On the other, Android Neural Networks API: Hmm?
tensorflow  distributed  distributedComputing  NN  architecture  platform 
december 2017 by psychemedia
Notes on Distributed Systems for Young Bloods – Something Similar
New systems engineers will find the Fallacies of Distributed Computing and the CAP theorem as part of their self-education. But these are abstract pieces without the direct, actionable advice the inexperienced engineer needs to start moving. It’s surprising how little context new engineers are given when they start out.

Below is a list of some lessons I’ve learned as a distributed systems engineer that are worth being told to a new engineer. Some are subtle, and some are surprising, but none are controversial. This list is for the new distributed systems engineer to guide their thinking about the field they are taking on. It’s not comprehensive, but it’s a good beginning.
programming  distributed  distributedcomputing  fallacies 
september 2017 by stiefkind
An off-grid social network | Hacker News
HN item on the scuttlebut lo-fi distributed social network for sailors
internet  socialnetworks  lofi  diy  sailing  hackernews  offgrid  distributedcomputing 
april 2017 by geephroh
Trumpet: Timely and Precise Triggers in Data Centers
As data centers grow larger and strive to provide tight performance
and availability SLAs, their monitoring infrastructure
must move from passive systems that provide aggregated
inputs to human operators, to active systems that enable programmed
control. In this paper, we propose Trumpet, an
event monitoring system that leverages CPU resources and
end-host programmability, to monitor every packet and report
events at millisecond timescales. Trumpet users can express
many network-wide events, and the system efficiently detects
these events using triggers at end-hosts. Using careful design,
Trumpet can evaluate triggers by inspecting every packet at
full line rate even on future generations of NICs, scale to
thousands of triggers per end-host while bounding packet
processing delay to a few microseconds, and report events
to a controller within 10 milliseconds, even in the presence
of attacks. We demonstrate these properties using an implementation
of Trumpet, and also show that it allows operators
to describe new network events such as detecting correlated
bursts and loss, identifying the root cause of transient congestion,
and detecting short-term anomalies at the scale of a data
center tenant.
networking  distributedcomputing  monitoring  datacenter  papers 
february 2017 by mikecb

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